Transient response of different algorithms-based 2-DOF PIDA controllers. by Erdal Eker (19251018)

Steady-state response of different algorithms based 2-DOF PIDA controllers. by Erdal Eker (19251018)

Time domain performance metrics of different algorithms based 2-DOF PIDA controllers. by Erdal Eker (19251018)

Best convergence curves of the algorithms with respect to iteration number. by Erdal Eker (19251018)

Adopted parameter ranges and the obtained best parameters via different algorithms. by Erdal Eker (19251018)

Statistical performance evaluation of algorithms and p-values obtained from Wilcoxon’s test. by Erdal Eker (19251018)

Optimized network structure. by Menaa Nawaz (14304920)

Data Sheet 1_Robust multi-objective optimization framework for performance-based seismic design of steel frame with energy dissipation system.docx by Yuting Cheng (11954209)

“…This study introduces a novel Robust Multi-objective Optimization framework for Performance-Based Seismic Design (RMO-PBSD). …”

Cuff-less Blood Pressure Measurement based on Four-wavelength PPG Signals by Liang yongbo (4822017)

“…<a href="https://www.mdpi.com/2079-6374/8/4/101" target="_blank"><b>Link</b></a></p><p dir="ltr">[12] Xuhao Dong Ziyi Wang, Liangli Cao, Zhencheng Chen*, <b>Yongbo Liang*</b>. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals [J]. …”

Reconstruction results of a proton density weighted knee image with fat suppression. by Chi Zhang (9857)

Table_1_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX by Maruf A. Tamal (18947776)

“…To bridge these gaps, this study aims to develop a more robust, effective, sophisticated, and reliable solution for phishing detection through the optimal feature vectorization algorithm (OFVA) and supervised machine learning (SML) classifiers.…”

Table_2_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX by Maruf A. Tamal (18947776)

“…To bridge these gaps, this study aims to develop a more robust, effective, sophisticated, and reliable solution for phishing detection through the optimal feature vectorization algorithm (OFVA) and supervised machine learning (SML) classifiers.…”

Block diagram of 2-DOF PIDA controller. by Erdal Eker (19251018)

“…A novel adaptive objective function (combining normalized overshoot, normalized settling time, and cumulative tracking error) guides the tuning process to achieve a balanced improvement in both transient and steady-state performance. The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 7. by Erdal Eker (19251018)

“…A novel adaptive objective function (combining normalized overshoot, normalized settling time, and cumulative tracking error) guides the tuning process to achieve a balanced improvement in both transient and steady-state performance. The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 10. by Erdal Eker (19251018)

“…A novel adaptive objective function (combining normalized overshoot, normalized settling time, and cumulative tracking error) guides the tuning process to achieve a balanced improvement in both transient and steady-state performance. The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Risk element category diagram. by Yao Hu (3479972)

“…It can be summarized that the algorithmic model has good accuracy and robustness. …”

S1 Data - by Yao Hu (3479972)

“…It can be summarized that the algorithmic model has good accuracy and robustness. …”

Airport risk levels. by Yao Hu (3479972)

“…It can be summarized that the algorithmic model has good accuracy and robustness. …”

Comparison results with other literature. by Yao Hu (3479972)

“…It can be summarized that the algorithmic model has good accuracy and robustness. …”

LSTM model validation results. by Yao Hu (3479972)

“…It can be summarized that the algorithmic model has good accuracy and robustness. …”